Making instructions “visible” on the interface: an approach to learning fault diagnosis skills through guided discovery

Δημοσίευση σε επιστημονικό περιοδικόSummarization: Operator training in fault diagnosis of complex systems has taken several forms including heuristics, decision flow charts and qualitative plant modelling. Having to comply to a specific learning strategy, however, may increase workload in remembering instructions, constrain people in accommodating their own styles and deny opportunities for exploiting other strategies. A means for increasing learning flexibility and adaptability would be to manipulate the design of the interface in ways that prompt operators to recall past instructions or develop their own strategies. In this study, instructions are madevisible on the interface by presenting trainees with a set of tell-tale signs derived from diagnostic heuristics. A group of subjects T (new) was trained in using this interface while verbal instructions (e.g. plant theory) were provided to guide discovery of diagnostic rules; a second group T (old) received the same plant theory but practised on a conventional interface. Two other groups used the conventional interface and were trained to apply a set of heuristics with or without the support of a plant theory (H+T and H groups, respectively). Making instructions visible helped the T (new) group to achieve higher accuracy scores than the T (old) group on a subset of fault scenarios. On a near-transfer task, both the T (new) and T (old) groups were superior to the heuristics (H) group. On transfer to another plant, the T (new) group maintained superiority to the heuristics (H) group and exceeded the T (old) group only in a subset of fault scenarios; the differences between theT (new) and the H+T groups were not significant. The results may indicate that making instructionsvisible could enhance acquisition and also the transfer of complex skills while allowing for flexibility and adaptability in the learning environment.Παρουσιάστηκε στο: International Journal of Human-Computer Studie